Overview

Dataset statistics

Number of variables12
Number of observations5840
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory547.6 KiB
Average record size in memory96.0 B

Variable types

Text1
Categorical2
Numeric8
Boolean1

Alerts

Grams has constant value ""Constant
Calories is highly overall correlated with Calories_per_Gram and 6 other fieldsHigh correlation
Calories_per_Gram is highly overall correlated with Calories and 6 other fieldsHigh correlation
Carbs is highly overall correlated with Calories and 2 other fieldsHigh correlation
Fat is highly overall correlated with Calories and 4 other fieldsHigh correlation
Fiber is highly overall correlated with CarbsHigh correlation
Is_low_calorie is highly overall correlated with Calories and 1 other fieldsHigh correlation
Protein is highly overall correlated with Calories and 4 other fieldsHigh correlation
Protein_Percent is highly overall correlated with Calories and 4 other fieldsHigh correlation
Sat.Fat is highly overall correlated with Calories and 4 other fieldsHigh correlation
Is_low_calorie is highly imbalanced (56.6%)Imbalance
Protein has 615 (10.5%) zerosZeros
Fat has 1078 (18.5%) zerosZeros
Sat.Fat has 1714 (29.3%) zerosZeros
Fiber has 2163 (37.0%) zerosZeros
Carbs has 682 (11.7%) zerosZeros
Protein_Percent has 615 (10.5%) zerosZeros

Reproduction

Analysis started2023-12-10 07:10:36.465001
Analysis finished2023-12-10 07:10:52.677313
Duration16.21 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Food
Text

Distinct5819
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size45.8 KiB
2023-12-10T12:40:53.118406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length184
Median length103
Mean length34.73476
Min length3

Characters and Unicode

Total characters202851
Distinct characters70
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5799 ?
Unique (%)99.3%

Sample

1st rowCows' milk
2nd rowMilk skim
3rd rowButtermilk
4th rowEvaporated, undiluted
5th rowFortified milk
ValueCountFrequency (%)
with 1298
 
4.1%
fat 919
 
2.9%
or 892
 
2.8%
and 616
 
1.9%
cooked 590
 
1.9%
added 546
 
1.7%
as 500
 
1.6%
to 493
 
1.6%
ns 484
 
1.5%
cheese 412
 
1.3%
Other values (1664) 24886
78.7%
2023-12-10T12:40:54.101707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25810
 
12.7%
e 20923
 
10.3%
a 15396
 
7.6%
o 12668
 
6.2%
t 11779
 
5.8%
r 11259
 
5.6%
, 10450
 
5.2%
d 9982
 
4.9%
i 9503
 
4.7%
n 8423
 
4.2%
Other values (60) 66658
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 156812
77.3%
Space Separator 25810
 
12.7%
Other Punctuation 10890
 
5.4%
Uppercase Letter 8192
 
4.0%
Dash Punctuation 701
 
0.3%
Decimal Number 230
 
0.1%
Close Punctuation 108
 
0.1%
Open Punctuation 108
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 20923
13.3%
a 15396
 
9.8%
o 12668
 
8.1%
t 11779
 
7.5%
r 11259
 
7.2%
d 9982
 
6.4%
i 9503
 
6.1%
n 8423
 
5.4%
s 7825
 
5.0%
c 6965
 
4.4%
Other values (16) 42089
26.8%
Uppercase Letter
ValueCountFrequency (%)
C 1431
17.5%
S 1270
15.5%
P 985
12.0%
N 751
9.2%
B 599
7.3%
F 476
 
5.8%
R 408
 
5.0%
T 332
 
4.1%
M 322
 
3.9%
G 219
 
2.7%
Other values (16) 1399
17.1%
Other Punctuation
ValueCountFrequency (%)
, 10450
96.0%
/ 218
 
2.0%
; 113
 
1.0%
% 63
 
0.6%
' 23
 
0.2%
" 17
 
0.2%
. 6
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 99
43.0%
1 75
32.6%
2 36
 
15.7%
5 8
 
3.5%
4 7
 
3.0%
3 4
 
1.7%
9 1
 
0.4%
Space Separator
ValueCountFrequency (%)
25810
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 701
100.0%
Close Punctuation
ValueCountFrequency (%)
) 108
100.0%
Open Punctuation
ValueCountFrequency (%)
( 108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 165004
81.3%
Common 37847
 
18.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 20923
12.7%
a 15396
 
9.3%
o 12668
 
7.7%
t 11779
 
7.1%
r 11259
 
6.8%
d 9982
 
6.0%
i 9503
 
5.8%
n 8423
 
5.1%
s 7825
 
4.7%
c 6965
 
4.2%
Other values (42) 50281
30.5%
Common
ValueCountFrequency (%)
25810
68.2%
, 10450
27.6%
- 701
 
1.9%
/ 218
 
0.6%
; 113
 
0.3%
) 108
 
0.3%
( 108
 
0.3%
0 99
 
0.3%
1 75
 
0.2%
% 63
 
0.2%
Other values (8) 102
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 202851
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
25810
 
12.7%
e 20923
 
10.3%
a 15396
 
7.6%
o 12668
 
6.2%
t 11779
 
5.8%
r 11259
 
5.6%
, 10450
 
5.2%
d 9982
 
4.9%
i 9503
 
4.7%
n 8423
 
4.2%
Other values (60) 66658
32.9%

Grams
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.8 KiB
100
5840 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17520
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 5840
100.0%

Length

2023-12-10T12:40:54.413371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T12:40:54.641108image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
100 5840
100.0%

Most occurring characters

ValueCountFrequency (%)
0 11680
66.7%
1 5840
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17520
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11680
66.7%
1 5840
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 17520
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11680
66.7%
1 5840
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11680
66.7%
1 5840
33.3%

Calories
Real number (ℝ)

HIGH CORRELATION 

Distinct617
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201.87295
Minimum0
Maximum902
Zeros37
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size45.8 KiB
2023-12-10T12:40:54.927202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27
Q179
median166.5
Q3282
95-th percentile497
Maximum902
Range902
Interquartile range (IQR)203

Descriptive statistics

Standard deviation152.3006
Coefficient of variation (CV)0.75443789
Kurtosis1.6211788
Mean201.87295
Median Absolute Deviation (MAD)99.5
Skewness1.1667981
Sum1178938
Variance23195.472
MonotonicityNot monotonic
2023-12-10T12:40:55.252017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 41
 
0.7%
65 40
 
0.7%
0 37
 
0.6%
51 35
 
0.6%
64 33
 
0.6%
134 33
 
0.6%
48 33
 
0.6%
62 32
 
0.5%
127 32
 
0.5%
44 31
 
0.5%
Other values (607) 5493
94.1%
ValueCountFrequency (%)
0 37
0.6%
1 26
0.4%
2 12
 
0.2%
3 11
 
0.2%
4 9
 
0.2%
5 14
 
0.2%
6 7
 
0.1%
7 5
 
0.1%
8 4
 
0.1%
9 4
 
0.1%
ValueCountFrequency (%)
902 1
 
< 0.1%
900 16
0.3%
896 1
 
< 0.1%
895 1
 
< 0.1%
893 3
 
0.1%
822 1
 
< 0.1%
767 1
 
< 0.1%
748 2
 
< 0.1%
745 1
 
< 0.1%
740 2
 
< 0.1%

Protein
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6232877
Minimum-2
Maximum102
Zeros615
Zeros (%)10.5%
Negative1
Negative (%)< 0.1%
Memory size45.8 KiB
2023-12-10T12:40:55.577112image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0
Q12
median5
Q312
95-th percentile26
Maximum102
Range104
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.9967096
Coefficient of variation (CV)1.0433039
Kurtosis7.1642142
Mean8.6232877
Median Absolute Deviation (MAD)4
Skewness1.8411842
Sum50360
Variance80.940783
MonotonicityNot monotonic
2023-12-10T12:40:55.913857image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 621
 
10.6%
0 615
 
10.5%
1 567
 
9.7%
3 494
 
8.5%
4 334
 
5.7%
5 300
 
5.1%
6 243
 
4.2%
7 239
 
4.1%
8 235
 
4.0%
9 231
 
4.0%
Other values (48) 1961
33.6%
ValueCountFrequency (%)
-2 1
 
< 0.1%
0 615
10.5%
1 567
9.7%
2 621
10.6%
3 494
8.5%
4 334
5.7%
5 300
5.1%
6 243
 
4.2%
7 239
 
4.1%
8 235
 
4.0%
ValueCountFrequency (%)
102 1
< 0.1%
101 1
< 0.1%
78 2
< 0.1%
76 1
< 0.1%
64 1
< 0.1%
63 2
< 0.1%
61 1
< 0.1%
59 1
< 0.1%
57 1
< 0.1%
56 2
< 0.1%

Fat
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2383562
Minimum0
Maximum103
Zeros1078
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size45.8 KiB
2023-12-10T12:40:56.250563image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q313
95-th percentile30
Maximum103
Range103
Interquartile range (IQR)12

Descriptive statistics

Standard deviation12.456784
Coefficient of variation (CV)1.3483767
Kurtosis16.313757
Mean9.2383562
Median Absolute Deviation (MAD)5
Skewness3.3249942
Sum53952
Variance155.17147
MonotonicityNot monotonic
2023-12-10T12:40:56.592949image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1078
18.5%
3 510
 
8.7%
1 422
 
7.2%
2 380
 
6.5%
4 354
 
6.1%
5 256
 
4.4%
7 229
 
3.9%
8 209
 
3.6%
6 205
 
3.5%
11 196
 
3.4%
Other values (70) 2001
34.3%
ValueCountFrequency (%)
0 1078
18.5%
1 422
 
7.2%
2 380
 
6.5%
3 510
8.7%
4 354
 
6.1%
5 256
 
4.4%
6 205
 
3.5%
7 229
 
3.9%
8 209
 
3.6%
9 165
 
2.8%
ValueCountFrequency (%)
103 1
 
< 0.1%
101 1
 
< 0.1%
100 21
0.4%
99 2
 
< 0.1%
82 2
 
< 0.1%
81 6
 
0.1%
80 1
 
< 0.1%
79 2
 
< 0.1%
78 3
 
0.1%
76 1
 
< 0.1%

Sat.Fat
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0695205
Minimum0
Maximum104
Zeros1714
Zeros (%)29.3%
Negative0
Negative (%)0.0%
Memory size45.8 KiB
2023-12-10T12:40:56.930887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile11
Maximum104
Range104
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.6683739
Coefficient of variation (CV)1.8466643
Kurtosis76.827994
Mean3.0695205
Median Absolute Deviation (MAD)1
Skewness6.7468791
Sum17926
Variance32.130463
MonotonicityNot monotonic
2023-12-10T12:40:57.281518image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1714
29.3%
1 1352
23.2%
2 701
12.0%
3 541
 
9.3%
4 373
 
6.4%
5 288
 
4.9%
6 172
 
2.9%
7 105
 
1.8%
8 105
 
1.8%
9 96
 
1.6%
Other values (44) 393
 
6.7%
ValueCountFrequency (%)
0 1714
29.3%
1 1352
23.2%
2 701
12.0%
3 541
 
9.3%
4 373
 
6.4%
5 288
 
4.9%
6 172
 
2.9%
7 105
 
1.8%
8 105
 
1.8%
9 96
 
1.6%
ValueCountFrequency (%)
104 1
 
< 0.1%
102 1
 
< 0.1%
88 1
 
< 0.1%
84 1
 
< 0.1%
82 1
 
< 0.1%
71 1
 
< 0.1%
68 1
 
< 0.1%
64 1
 
< 0.1%
62 1
 
< 0.1%
51 3
0.1%

Fiber
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6907534
Minimum0
Maximum104
Zeros2163
Zeros (%)37.0%
Negative0
Negative (%)0.0%
Memory size45.8 KiB
2023-12-10T12:40:57.583359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum104
Range104
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.1237945
Coefficient of variation (CV)1.8475755
Kurtosis395.69037
Mean1.6907534
Median Absolute Deviation (MAD)1
Skewness14.074437
Sum9874
Variance9.7580923
MonotonicityNot monotonic
2023-12-10T12:40:57.867592image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 2163
37.0%
1 1416
24.2%
2 1098
18.8%
3 455
 
7.8%
4 262
 
4.5%
6 98
 
1.7%
5 94
 
1.6%
7 59
 
1.0%
10 56
 
1.0%
8 48
 
0.8%
Other values (18) 91
 
1.6%
ValueCountFrequency (%)
0 2163
37.0%
1 1416
24.2%
2 1098
18.8%
3 455
 
7.8%
4 262
 
4.5%
5 94
 
1.6%
6 98
 
1.7%
7 59
 
1.0%
8 48
 
0.8%
9 29
 
0.5%
ValueCountFrequency (%)
104 1
 
< 0.1%
102 1
 
< 0.1%
43 1
 
< 0.1%
40 1
 
< 0.1%
37 1
 
< 0.1%
29 1
 
< 0.1%
27 3
0.1%
23 1
 
< 0.1%
20 2
< 0.1%
19 2
< 0.1%

Carbs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct104
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.15
Minimum0
Maximum128
Zeros682
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size45.8 KiB
2023-12-10T12:40:58.169921image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median13
Q329
95-th percentile70
Maximum128
Range128
Interquartile range (IQR)24

Descriptive statistics

Standard deviation21.97581
Coefficient of variation (CV)1.0390454
Kurtosis1.1579573
Mean21.15
Median Absolute Deviation (MAD)10
Skewness1.3731536
Sum123516
Variance482.93622
MonotonicityNot monotonic
2023-12-10T12:40:58.501963image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 682
 
11.7%
13 237
 
4.1%
4 214
 
3.7%
10 202
 
3.5%
5 187
 
3.2%
6 184
 
3.2%
12 175
 
3.0%
1 164
 
2.8%
8 163
 
2.8%
16 161
 
2.8%
Other values (94) 3471
59.4%
ValueCountFrequency (%)
0 682
11.7%
1 164
 
2.8%
2 121
 
2.1%
3 157
 
2.7%
4 214
 
3.7%
5 187
 
3.2%
6 184
 
3.2%
7 159
 
2.7%
8 163
 
2.8%
9 155
 
2.7%
ValueCountFrequency (%)
128 1
 
< 0.1%
105 1
 
< 0.1%
103 1
 
< 0.1%
100 9
0.2%
99 4
0.1%
98 6
0.1%
97 1
 
< 0.1%
96 1
 
< 0.1%
95 4
0.1%
94 1
 
< 0.1%

Category
Categorical

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size45.8 KiB
Miscellaneous
3072 
Meat, Poultry
966 
Breads, cereals, fastfood,grains
897 
Dairy products
481 
Potato
 
105
Other values (4)
319 

Length

Max length33
Median length13
Mean length15.694692
Min length6

Characters and Unicode

Total characters91657
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDairy products
2nd rowDairy products
3rd rowDairy products
4th rowDairy products
5th rowDairy products

Common Values

ValueCountFrequency (%)
Miscellaneous 3072
52.6%
Meat, Poultry 966
 
16.5%
Breads, cereals, fastfood,grains 897
 
15.4%
Dairy products 481
 
8.2%
Potato 105
 
1.8%
Cookie 100
 
1.7%
Coffee 91
 
1.6%
Vegetables 71
 
1.2%
Fruits 57
 
1.0%

Length

2023-12-10T12:40:58.808637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T12:40:59.086326image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
miscellaneous 3072
33.8%
meat 966
 
10.6%
poultry 966
 
10.6%
breads 897
 
9.9%
cereals 897
 
9.9%
fastfood,grains 897
 
9.9%
dairy 481
 
5.3%
products 481
 
5.3%
potato 105
 
1.2%
cookie 100
 
1.1%
Other values (3) 219
 
2.4%

Most occurring characters

ValueCountFrequency (%)
s 10341
11.3%
e 10296
11.2%
a 8283
 
9.0%
l 8078
 
8.8%
o 6814
 
7.4%
r 4676
 
5.1%
i 4607
 
5.0%
u 4576
 
5.0%
c 4450
 
4.9%
4138
 
4.5%
Other values (17) 25398
27.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 77056
84.1%
Uppercase Letter 6806
 
7.4%
Space Separator 4138
 
4.5%
Other Punctuation 3657
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 10341
13.4%
e 10296
13.4%
a 8283
10.7%
l 8078
10.5%
o 6814
8.8%
r 4676
6.1%
i 4607
6.0%
u 4576
5.9%
c 4450
5.8%
n 3969
 
5.2%
Other values (8) 10966
14.2%
Uppercase Letter
ValueCountFrequency (%)
M 4038
59.3%
P 1071
 
15.7%
B 897
 
13.2%
D 481
 
7.1%
C 191
 
2.8%
V 71
 
1.0%
F 57
 
0.8%
Space Separator
ValueCountFrequency (%)
4138
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3657
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 83862
91.5%
Common 7795
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 10341
12.3%
e 10296
12.3%
a 8283
9.9%
l 8078
9.6%
o 6814
 
8.1%
r 4676
 
5.6%
i 4607
 
5.5%
u 4576
 
5.5%
c 4450
 
5.3%
M 4038
 
4.8%
Other values (15) 17703
21.1%
Common
ValueCountFrequency (%)
4138
53.1%
, 3657
46.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91657
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 10341
11.3%
e 10296
11.2%
a 8283
 
9.0%
l 8078
 
8.8%
o 6814
 
7.4%
r 4676
 
5.1%
i 4607
 
5.0%
u 4576
 
5.0%
c 4450
 
4.9%
4138
 
4.5%
Other values (17) 25398
27.7%

Calories_per_Gram
Real number (ℝ)

HIGH CORRELATION 

Distinct617
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0187295
Minimum0
Maximum9.02
Zeros37
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size45.8 KiB
2023-12-10T12:40:59.459525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.27
Q10.79
median1.665
Q32.82
95-th percentile4.97
Maximum9.02
Range9.02
Interquartile range (IQR)2.03

Descriptive statistics

Standard deviation1.523006
Coefficient of variation (CV)0.75443789
Kurtosis1.6211788
Mean2.0187295
Median Absolute Deviation (MAD)0.995
Skewness1.1667981
Sum11789.38
Variance2.3195472
MonotonicityNot monotonic
2023-12-10T12:40:59.767407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 41
 
0.7%
0.65 40
 
0.7%
0 37
 
0.6%
0.51 35
 
0.6%
0.64 33
 
0.6%
1.34 33
 
0.6%
0.48 33
 
0.6%
0.62 32
 
0.5%
1.27 32
 
0.5%
0.44 31
 
0.5%
Other values (607) 5493
94.1%
ValueCountFrequency (%)
0 37
0.6%
0.01 26
0.4%
0.02 12
 
0.2%
0.03 11
 
0.2%
0.04 9
 
0.2%
0.05 14
 
0.2%
0.06 7
 
0.1%
0.07 5
 
0.1%
0.08 4
 
0.1%
0.09 4
 
0.1%
ValueCountFrequency (%)
9.02 1
 
< 0.1%
9 16
0.3%
8.96 1
 
< 0.1%
8.95 1
 
< 0.1%
8.93 3
 
0.1%
8.22 1
 
< 0.1%
7.67 1
 
< 0.1%
7.48 2
 
< 0.1%
7.45 1
 
< 0.1%
7.4 2
 
< 0.1%

Protein_Percent
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6232877
Minimum-2
Maximum102
Zeros615
Zeros (%)10.5%
Negative1
Negative (%)< 0.1%
Memory size45.8 KiB
2023-12-10T12:41:00.091145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0
Q12
median5
Q312
95-th percentile26
Maximum102
Range104
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.9967096
Coefficient of variation (CV)1.0433039
Kurtosis7.1642142
Mean8.6232877
Median Absolute Deviation (MAD)4
Skewness1.8411842
Sum50360
Variance80.940783
MonotonicityNot monotonic
2023-12-10T12:41:00.424509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 621
 
10.6%
0 615
 
10.5%
1 567
 
9.7%
3 494
 
8.5%
4 334
 
5.7%
5 300
 
5.1%
6 243
 
4.2%
7 239
 
4.1%
8 235
 
4.0%
9 231
 
4.0%
Other values (48) 1961
33.6%
ValueCountFrequency (%)
-2 1
 
< 0.1%
0 615
10.5%
1 567
9.7%
2 621
10.6%
3 494
8.5%
4 334
5.7%
5 300
5.1%
6 243
 
4.2%
7 239
 
4.1%
8 235
 
4.0%
ValueCountFrequency (%)
102 1
< 0.1%
101 1
< 0.1%
78 2
< 0.1%
76 1
< 0.1%
64 1
< 0.1%
63 2
< 0.1%
61 1
< 0.1%
59 1
< 0.1%
57 1
< 0.1%
56 2
< 0.1%

Is_low_calorie
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
False
5319 
True
 
521
ValueCountFrequency (%)
False 5319
91.1%
True 521
 
8.9%
2023-12-10T12:41:00.678705image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Interactions

2023-12-10T12:40:50.030168image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:38.387589image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:40.045579image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:41.701338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:43.354803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:45.035156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:46.694469image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:48.385523image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:50.233763image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:38.596248image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:40.247777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:41.908525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:43.563994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:45.257498image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:46.904993image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:48.581707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:50.441329image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:38.797436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:40.452503image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:42.115897image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:43.767381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:45.457990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:47.114179image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:48.791417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:50.644521image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:39.005187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:40.658004image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:42.318430image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:43.971894image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:45.660211image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:47.323591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:48.996138image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:50.855986image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:39.213298image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:40.861994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:42.525056image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:44.172079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:45.864944image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:47.540184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:49.200345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:51.072185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:39.425701image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:41.066462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:42.731250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:44.375132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:46.066553image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:47.744370image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:49.409038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:51.291396image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:39.635895image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:41.284672image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:42.943437image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:44.603759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:46.277080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:47.962558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:49.620279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:51.500098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:39.837837image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:41.489149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:43.147621image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:44.819378image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:46.487127image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:48.172751image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-10T12:40:49.818463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-12-10T12:41:00.861436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
CaloriesCalories_per_GramCarbsCategoryFatFiberIs_low_calorieProteinProtein_PercentSat.Fat
Calories1.0001.0000.5470.2100.8050.2610.5030.5150.5150.718
Calories_per_Gram1.0001.0000.5470.2100.8050.2610.5030.5150.5150.718
Carbs0.5470.5471.0000.2210.1690.5820.335-0.107-0.1070.117
Category0.2100.2100.2211.000-0.1390.0230.350-0.186-0.186-0.129
Fat0.8050.8050.169-0.1391.0000.1190.2100.5570.5570.921
Fiber0.2610.2610.5820.0230.1191.0000.014-0.036-0.0360.023
Is_low_calorie0.5030.5030.3350.3500.2100.0141.000-0.396-0.396-0.379
Protein0.5150.515-0.107-0.1860.557-0.036-0.3961.0001.0000.502
Protein_Percent0.5150.515-0.107-0.1860.557-0.036-0.3961.0001.0000.502
Sat.Fat0.7180.7180.117-0.1290.9210.023-0.3790.5020.5021.000

Missing values

2023-12-10T12:40:51.793463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T12:40:52.464282image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

FoodGramsCaloriesProteinFatSat.FatFiberCarbsCategoryCalories_per_GramProtein_PercentIs_low_calorie
0Cows' milk1006834405Dairy products0.683No
1Milk skim1003740005Dairy products0.374Yes
2Buttermilk1005242205Dairy products0.524No
3Evaporated, undiluted100137687010Dairy products1.376No
4Fortified milk1009763208Dairy products0.976No
5Powdered milk100500262723038Dairy products5.0026No
6skim, instant1003413500049Dairy products3.4135No
7skim, non-instant1003413500149Dairy products3.4135No
8Goats' milk1006834305Dairy products0.683No
9(1/2 cup ice cream)100128444013Dairy products1.284No
FoodGramsCaloriesProteinFatSat.FatFiberCarbsCategoryCalories_per_GramProtein_PercentIs_low_calorie
5830Tomatoes, cooked, as ingredient1002610025Miscellaneous0.261Yes
5831Onions, cooked, as ingredient10050100211Miscellaneous0.501No
5832Mushrooms, cooked, as ingredient1003640014Miscellaneous0.364Yes
5833Green pepper, cooked, as ingredient1002510025Miscellaneous0.251Yes
5834Red pepper, cooked, as ingredient1003310026Miscellaneous0.331Yes
5835Cabbage, cooked, as ingredient1003010036Miscellaneous0.301Yes
5836Cauliflower, cooked, as ingredient1003120025Miscellaneous0.312Yes
5837Eggplant, cooked, as ingredient1003110036Meat, Poultry0.311Yes
5838Green beans, cooked, as ingredient1003920037Miscellaneous0.392Yes
5839Summer squash, cooked, as ingredient1002510014Miscellaneous0.251Yes